1. Field of Disclosure
The disclosure generally relates to the field of controlling motion of a system, and more specifically, to controlling motion of a system to avoid collision.
2. Description of the Related Art
Collision avoidance has been an important and widely studied problem since the inception of robotics technology. The majority of early research in this area focused on obstacle avoidance, typically for applications involving mobile robot navigation and industrial manipulation. See A. A. Maciejewski and C. A. Klein, “Obstacle avoidance for kinematically redundant manipulators in dynamically varying environments”, International Journal of Robotics Research, 4:109-117 (1985); see also 0. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots”, The International Journal of Robotics Research (IJRR), 5(1):90-98 (1986), both of which are incorporated by reference herein in their entirety. In these applications, the workspace was often predefined, static, or slowly varying. Moreover, application developers typically adopted the philosophy of segregating the workspace of robots and people as a safety countermeasure to avoid collisions with people. Setting large collision distance thresholds could be tolerated for many of the early mobile navigation and industrial manipulation applications. The concept of artificial potential fields proved to be an effective approach for obstacle avoidance under such circumstances.
Today, the field of robotics is moving towards development of high degree of freedom, human-like, and personal robots, which are often designed to share a common workspace and physically interact with humans. Such robots are often highly redundant which fundamentally adds new capabilities (self-motion and subtask performance capability). However, increased redundancy has also added new challenges for constraining the internal motion to avoid joint limits and self collisions. With these challenges, researchers have become increasingly aware of the need for robust collision avoidance strategies to accommodate such applications.
In particular, self collision avoidance, which was largely overlooked or not required when obstacle avoidance strategies were first developed, has recently become an important topic of research. See H. Sugiura, M. Gienger, H. Janssen, and C. Goerick, “Real-time collision avoidance with whole body motion control for humanoid robots”, IEEE Int. Conf. on Intelligent Robots and Systems (IROS 2007), (2007); see also O. Stasse, A. Escande, N. Mansard, S. Miossec, P. Evrard, and A. Kheddar, “Real-time (self)-collision avoidance task on a hrp-2 humanoid robot”, In Proceedings of ICRA, pages 3200-3205, Pasadena Calif. (2008), both of which are incorporated by reference herein in their entirety. Self collision avoidance is especially challenging for humanoid robots performing humanlike tasks. The collision avoidance strategy must not only accommodate multiple colliding bodies simultaneously, but also tolerate smaller collision distances than those established for early obstacle avoidance algorithms. Furthermore, a self collision avoidance strategy should not significantly alter the reference or originally planned motion. This is particularly important in applications involving reproduction of robot motion from observed human motion, a problem often referred to as motion retargeting. See B. Dariush, M. Gienger, B. Jian, C. Goerick, and K. Fujimura, “Whole body humanoid control from human motion descriptors”, Int. Conf. Robotics and Automation (ICRA), Pasadena, Calif. (2008); see also A. Nakazawa, S. Nakaoka, K. Ikeuchi, and K. Yokoi, “Imitating human dance motions through motion structure analysis”, Intl. Conference on Intelligent Robots and Systems (IROS), pages 2539-2544, Lausanne, Switzerland (2002), both of which are incorporated by reference herein in their entirety.
Hence, there is lacking, inter alia, a system and method for motion control of robots and other articulated rigid body systems to avoid self collisions and obstacles in real-time.